Forskning
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Region Hovedstaden - en del af Københavns Universitetshospital
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Expertise

Computer science; machine learning; applied signal processing; big data; normal and pathological sleep; narcolepsy; Parkinson’s disease; electrophysiological signals (EEG, EOG, EMG); data-driven models and algorithms; programming; Matlab

Primære forskningsområder

Automatic identification of safer markers in the sleep that can help to improve diagnosis and treatment for various sleep disorders.

Development of data-driven algorithms and sleep models that can reflect much more details in the normal and pathological sleep than is currently possible by today’s manual methods.

Aktuel forskning

Automatic detection and analysis of the micro-sleep event “sleep spindles” in normal and pathological sleep.

Automatic detection and analysis of nocturnal eye movements in normal and pathological sleep.

Developing data-driven sleep models to extract specific markers for sleep-wake transitions and stability in normal and pathological sleep.

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